Neural Network Visualizer
Interactive visualization of neural network architecture and forward propagation
Network Architecture
Weight Colors
Click a neuron to see its details. Use animation to visualize forward propagation.
Network Statistics
4
Layers
13
Neurons
36
Weights
10
Biases
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What is a Neural Network?
A neural network is a computing system inspired by biological neural networks. It consists of layers of interconnected nodes (neurons), each learning to transform inputs into useful outputs.
Network Components
Neurons (Nodes)
Process inputs by applying weights, summing, and passing through an activation function.
Weights
Learnable parameters that determine connection strength between neurons.
Layers
Input receives data, hidden layers learn representations, output produces predictions.
How to Use
Select a preset network or customize layers
Click "Animate" to visualize forward propagation
Click neurons to see connection details
Green weights = positive, red = negative
Tip
The number of parameters grows quickly! A network with layers [100, 64, 10] has 100×64 + 64×10 = 7,040 weights plus biases.
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